Self-Scaling Stream Processing: A Bio-Inspired Approach to Resource Allocation through Dynamic Task Replication

Pierre-André Mudry, G. Tempesti
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引用次数: 6

Abstract

In this article, we show how the use of a bio-inspired dynamic task replication algorithm, in the context of stream processing, can be used to significantly improve the performance of embedded programs. We also show that this programming methodology, which is not tied to a particular implementation, can also be used as an heuristic for task mapping in the context of embedded multiprocessors systems. The technique was applied to a 36-processor system implemented on a scalable mesh of FPGAS for two different case studies: for AES encryption, it resulted in a ten-fold speedup compared to a static implementation, while for MJPEG compression a throughput multiplication of 11 was obtained.
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自扩展流处理:通过动态任务复制实现资源分配的仿生方法
在本文中,我们展示了如何在流处理的背景下使用生物启发的动态任务复制算法来显着提高嵌入式程序的性能。我们还展示了这种编程方法,它与特定的实现无关,也可以用作嵌入式多处理器系统上下文中任务映射的启发式方法。该技术应用于一个36处理器的系统,该系统在可扩展的fpga网格上实现,用于两个不同的案例研究:对于AES加密,与静态实现相比,它的速度提高了10倍,而对于MJPEG压缩,吞吐量增加了11倍。
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